Publication: Automatic detection of knives in infrared images
dc.contributor.author | Sumeth Yuenyong | en_US |
dc.contributor.author | Narit Hnoohom | en_US |
dc.contributor.author | Konlakorn Wongpatikaseree | en_US |
dc.contributor.other | Mahidol University | en_US |
dc.date.accessioned | 2019-08-23T10:56:45Z | |
dc.date.available | 2019-08-23T10:56:45Z | |
dc.date.issued | 2018-06-08 | en_US |
dc.description.abstract | © 2018 IEEE. The researchers present an experiment on automatic detection of concealed knives in infrared (IR) images. The researchers generated a dataset, called the IR dataset, which contained 8,527 images. The dataset was divided into two groups of IR images comprised of person without knife and person with a knife. Knives of different shapes and sizes were concealed under normal clothing, then images of person subjects carrying hidden knives were taken with a smartphone IR camera add-on. A deep neural network that was trained on natural image (GoogleNet dataset) was fine-tuned to classify the IR images as person, or person carrying hidden knife. The classification accuracy on a separate test set shows that hidden knives can be detected at 97.91% accuracy. | en_US |
dc.identifier.citation | 1st International ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI-NCON 2018. (2018), 65-68 | en_US |
dc.identifier.doi | 10.1109/ECTI-NCON.2018.8378283 | en_US |
dc.identifier.other | 2-s2.0-85049945756 | en_US |
dc.identifier.uri | https://repository.li.mahidol.ac.th/handle/20.500.14594/45626 | |
dc.rights | Mahidol University | en_US |
dc.rights.holder | SCOPUS | en_US |
dc.source.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85049945756&origin=inward | en_US |
dc.subject | Computer Science | en_US |
dc.subject | Engineering | en_US |
dc.title | Automatic detection of knives in infrared images | en_US |
dc.type | Conference Paper | en_US |
dspace.entity.type | Publication | |
mu.datasource.scopus | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85049945756&origin=inward | en_US |